Research Projects and PhD opportunities

My interests are in the intersection of electric power systems and information technology.

IT for energy Management

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Demand response

Historically, electricity has been available whenever it was required. This was achieved by varying the amount of generation to match the demand at all times. This requires very expensive over-provisioning of generation capacity. More importantly, as society makes the transition to sustainable energy sources such as wind and solar, generation can only occur when the resource is available. Demand response (DR) refers to any system in which the supply-demand balance is achied by deferring certain demands. Typically these demands are from things such as charging batteries or pumping water through swimming pools, which can be done at any time. This project will investigate important open questions in demand response systems, such as:

How do we provide suitable incentives to get people to take part in DR systems?
What effect do the short-term incentives to be part of a DR system have on long-term behaviour, such as the choice of which appliances to purchase.
How much is total demand reduced rather than simply rescheduled by DR? Can DR be seen as a queueing system for demand?
How does DR interact with the distribution network?

Energy storage management

As noted above, there is a growing need for technology to match electricity consumption to non-controllable generation. Energy storage is one such technology. Storage technology such as batteries and pumped hydro can be used on very short timescales in frequency regulation markets or to provide spinning reserve, or on longer timescales to store solar energy for night time, or store wind energy for cloudy days. Optimal management of storage is made difficult by imprecise forecasts of future load and future generation. For this reasons, techniques such as Lyapunov optimization have been applied for storage management. This poject will addess stoage management of ealistic stoage devices, with impefectiosn such as inefficiency and self-dischage. In paticular, it will look at management of a potfolio of devices with different impefections, and investigate how much benefit comes from having diversity among storage types.

Network inference

The power grid used to be centrally managed infrastructure, with a small number of generators whose states could be observed, but a sparsely monitored transmission and distribution network. With the advent of smart meters and distributed generation, we have many more sources of information but also many more states that need to be observed. This project will explore the use smart meter data for tasks such as:

Estimating the size and orientation of domestic solar panels, based on daily variation in energy consumption.
Estimating the true electricity consumption of houses that have local generation.

Load disaggregation / non-intrusive load monitoring

In order to save electricity, it is important to know where the electricity is going. Putting an electricity meter on every device in a house is expensive, and so there has been considerable interest in estimating the consumption of individual devices based on the measurement of a house's total energy consumption. Current techniques for this either require a large amount of manual intervention to set up, or are unreliable -- or both. This project will:

Develop methods for "learning" device characteristics with minimal intervention.
Investigate new characteristics by which devices can be identified, such as the spectrum of the noise in their steady-state power consumption.

Energy management of IT systems

Dynamic capacity provisioning

Data centres are typically designed to process their peak workload, but workload varies substantially throughout the day. As a result, many people have proposed that some servers be turned off during periods of low load. However, there is a cost for turning servers on and off, and it is not known in advance how long the load will be low for. This poject will investigate popeties of LCP, an on-line algoithm that was ecently poposed to solve this poblem and othe "smoothed online convex optimization" poblems. Specific questions are:

For what classes of cost functions is LCP better than 3-competitive?
How can partial knowledge of future loads best be used by LCP, what what performance guarantees can it provide?
Can LCP incorporate randomization to improve its average competitive ratio?

Geographical load balancing

Many systems, such as Microsoft's Azure, spread work among many geographically dispersed data centres. The ease with which data can be transported makes this an opportunity to use data centres as demand-response participants. Load can be shifted to data centres where the availability of renewable energy temporarily exceeds the demand from inflexible sources. However, rerouting work often requires large databases to be migrated, and so there can be a substantial cost to changing the allocation of work. This makes it hard to determine the optimal routing without knowing future conditions. This project will explore the use of "metrical task system" algorithms to distribute work between data centres without requiring any future knowledge.

Selection requirements

I am looking for students who

Want to improve the sustainability of global energy use
Have an excellent mathematical background, or a strong background in both mathematics and programming
Are committed to the pursuit of excellence

Email:
Tel:+61 3 990 58669